Bulut Miray Gunay, Unal Sencer, Hammad Mohamed, Pławiak Paweł
Department of Electricity, Malatya Turgut Ozal University, Turkey.
Department of Electrical and Electronics Engineering, Firat University, Turkey.
PLoS One. 2025 Feb 12;20(2):e0314154. doi: 10.1371/journal.pone.0314154. eCollection 2025.
Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachycardia) or too slow (bradycardia), irregular heartbeats (like atrial fibrillation-AF, ventricular fibrillation-VF), or the initiation of heartbeats in different areas from the norm (extrasystole). Arrhythmias can disrupt the balanced circulation, leading to serious complications like heart attacks, strokes, and sudden death. Medical devices like electrocardiography (ECG) and Holter monitors are commonly used for diagnosing and monitoring cardiac rhythm disorders. However, in recent years, the development of wearable devices has played a significant role in the detection and diagnosis of rhythm disorders through the use of photoplethysmography (PPG) signals. Wearable devices enable patients to continuously monitor their health status and allow doctors to provide earlier diagnoses and interventions. In this study, a 1D-CNN model is proposed to detect arrhythmias using PPG signals. A dataset prepared by the University of Massachusetts Medical Center (UMMC) containing both ECG and PPG signal data was utilized. In this dataset, ECG signals are filtered with a bandpass filter and raw PPG signals are divided into 30-second segments. Accuracy values were obtained by classifying ECG and PPG signals using a 1D CNN model. ECG signals were used as a reference. The proposed model achieved a 95.17% accuracy rate in detecting normal sinus rhythm (NSR), atrial fibrillation (AF), and premature atrial contractions (PAC) from PPG signals. Datasets are available for download on https://www.synapse.org/pulsewatch. The codes used in this study are available on the https://github.com/miraygunay/PPG-Code.git website.
心律失常可通过多种方式表现出来，例如心率过快（心动过速）或过慢（心动过缓）、心跳不规则（如心房颤动-AF、心室颤动-VF），或者心跳起始部位与正常情况不同（早搏）。心律失常会扰乱血液循环平衡，导致严重并发症，如心脏病发作、中风和猝死。心电图（ECG）和动态心电图监测仪等医疗设备常用于诊断和监测心律失常。然而，近年来，可穿戴设备的发展通过使用光电容积脉搏波描记法（PPG）信号在心律失常的检测和诊断中发挥了重要作用。可穿戴设备使患者能够持续监测自身健康状况，并让医生能够进行更早的诊断和干预。在本研究中，提出了一种一维卷积神经网络（1D-CNN）模型，用于使用PPG信号检测心律失常。使用了由马萨诸塞大学医学中心（UMMC）准备的一个包含ECG和PPG信号数据的数据集。在该数据集中，ECG信号通过带通滤波器进行滤波，原始PPG信号被分成30秒的片段。通过使用一维卷积神经网络模型对ECG和PPG信号进行分类来获得准确率值。以ECG信号作为参考。所提出的模型在从PPG信号中检测正常窦性心律（NSR）、心房颤动（AF）和房性早搏（PAC）方面达到了95.17%的准确率。数据集可在https://www.synapse.org/pulsewatch上下载。本研究中使用的代码可在https://github.com/miraygunay/PPG-Code.git网站上获取。
